Can optical proximity correction solution be learned? The learning limit and a general learning framework

Xuelong Shi; Yan Yan; Chen Li; Mingyang Xia; Bingyang Pan; Ying Gao; Wei Yuan

doi:10.1117/1.JMM.21.4.043203

29 November 2022 Can optical proximity correction solution be learned? The learning limit and a general learning framework

Xuelong Shi, Yan Yan, Chen Li, Mingyang Xia, Bingyang Pan, Ying Gao, Wei Yuan

Author Affiliations +

Journal of Micro/Nanopatterning, Materials, and Metrology, Vol. 21, Issue 4, 043203 (November 2022). https://doi.org/10.1117/1.JMM.21.4.043203

Abstract

Background

Optical proximity correction (OPC) is an indispensable technology that has been propelling the advancement of computational lithography technology. To tightly control edge placement error (EPE) and maintain lithography process window, the demands on OPC computational resources and OPC turnaround time are growing rapidly with alarming acceleration. To tame the trend, machine learning technologies have been explored; however, an in-depth discussion on OPC solution learning limit is still lacking.

Aim

We aim to present an in-depth discussion on OPC solution learning limit and propose a general machine learning OPC framework that can be extended to curvilinear mask OPC technology.

Approach

In this study, we first investigate the machine learning OPC learning limit by examining noninverse lithography technology (non-ILT) OPC solution space characteristics inherited from edge segmentation and control point setting rules and then propose a general machine learning OPC framework that can take full advantage of deep convolution neural network (DCNN) learning capability while being able to preserve mask data high resolution.

Results

With this machine learning OPC framework, we have achieved models with average absolute model error <1 nm for 14-nm metal layer. With single GPU, the average time for machine learning OPC models to produce results of 3840 nm × 3840 nm area is 8.74 ms for single channel input model and 12.65 ms for six channels input model.

Conclusions

For non-ILT OPC solution, there is an intrinsic learning limit inherited from edge segmentation rules. Machine learning OPC models should be content with learning low order OPC solutions. This intrinsic learning limit of non-ILT OPC solution may diminish for ILT OPC solution when the constraint on degrees of freedom of OPC solution is lifted. The machine learning OPC framework we proposed is general and extendable to curvilinear OPC technology.

Citation Download Citation

Xuelong Shi, Yan Yan, Chen Li, Mingyang Xia, Bingyang Pan, Ying Gao, and Wei Yuan "Can optical proximity correction solution be learned? The learning limit and a general learning framework," Journal of Micro/Nanopatterning, Materials, and Metrology 21(4), 043203 (29 November 2022). https://doi.org/10.1117/1.JMM.21.4.043203

Received: 11 August 2022; Accepted: 2 November 2022; Published: 29 November 2022

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